{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "thorough-street",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aquatic-bulletin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Channel  Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "0        2       3  12669  9656     7561     214              2674        1338\n",
       "1        2       3   7057  9810     9568    1762              3293        1776\n",
       "2        2       3   6353  8808     7684    2405              3516        7844\n",
       "3        1       3  13265  1196     4221    6404               507        1788\n",
       "4        2       3  22615  5410     7198    3915              1777        5185"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm=pd.read_csv(\"wholesale customers data.csv\")\n",
    "cm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "earned-chart",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 440 entries, 0 to 439\n",
      "Data columns (total 8 columns):\n",
      " #   Column            Non-Null Count  Dtype\n",
      "---  ------            --------------  -----\n",
      " 0   Channel           440 non-null    int64\n",
      " 1   Region            440 non-null    int64\n",
      " 2   Fresh             440 non-null    int64\n",
      " 3   Milk              440 non-null    int64\n",
      " 4   Grocery           440 non-null    int64\n",
      " 5   Frozen            440 non-null    int64\n",
      " 6   Detergents_Paper  440 non-null    int64\n",
      " 7   Delicassen        440 non-null    int64\n",
      "dtypes: int64(8)\n",
      "memory usage: 27.6 KB\n"
     ]
    }
   ],
   "source": [
    "cm.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "nasty-gravity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.322727</td>\n",
       "      <td>2.543182</td>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.468052</td>\n",
       "      <td>0.774272</td>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Channel      Region          Fresh          Milk       Grocery  \\\n",
       "count  440.000000  440.000000     440.000000    440.000000    440.000000   \n",
       "mean     1.322727    2.543182   12000.297727   5796.265909   7951.277273   \n",
       "std      0.468052    0.774272   12647.328865   7380.377175   9503.162829   \n",
       "min      1.000000    1.000000       3.000000     55.000000      3.000000   \n",
       "25%      1.000000    2.000000    3127.750000   1533.000000   2153.000000   \n",
       "50%      1.000000    3.000000    8504.000000   3627.000000   4755.500000   \n",
       "75%      2.000000    3.000000   16933.750000   7190.250000  10655.750000   \n",
       "max      2.000000    3.000000  112151.000000  73498.000000  92780.000000   \n",
       "\n",
       "             Frozen  Detergents_Paper    Delicassen  \n",
       "count    440.000000        440.000000    440.000000  \n",
       "mean    3071.931818       2881.493182   1524.870455  \n",
       "std     4854.673333       4767.854448   2820.105937  \n",
       "min       25.000000          3.000000      3.000000  \n",
       "25%      742.250000        256.750000    408.250000  \n",
       "50%     1526.000000        816.500000    965.500000  \n",
       "75%     3554.250000       3922.000000   1820.250000  \n",
       "max    60869.000000      40827.000000  47943.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "accessible-radar",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.70833271, 0.53987376, 0.42274083, 0.01196489, 0.14950522,\n",
       "        0.07480852],\n",
       "       [0.44219826, 0.61470384, 0.59953989, 0.11040858, 0.20634248,\n",
       "        0.11128583],\n",
       "       [0.39655169, 0.5497918 , 0.47963217, 0.15011913, 0.2194673 ,\n",
       "        0.48961931],\n",
       "       ...,\n",
       "       [0.36446153, 0.38846468, 0.7585445 , 0.01096068, 0.37223685,\n",
       "        0.04682745],\n",
       "       [0.93773743, 0.1805304 , 0.20340427, 0.09459392, 0.01531   ,\n",
       "        0.19365326],\n",
       "       [0.67229603, 0.40960124, 0.60547651, 0.01567967, 0.11506466,\n",
       "        0.01254374]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import Normalizer\n",
    "train_data=cm.iloc[:,2:]\n",
    "norm_train_data=Normalizer().fit_transform(train_data)\n",
    "norm_train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "homeless-ambassador",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "beneficial-excerpt",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\asus\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'score')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters = [2,3,4,5,6]\n",
    "score=[]\n",
    "modle=[]\n",
    "for k in clusters:\n",
    "    kmeans_=KMeans(n_clusters=k)\n",
    "    kmeans_.fit(norm_train_data)\n",
    "    score.append(silhouette_score(norm_train_data,kmeans_.labels_))\n",
    "    modle.append(kmeans_)\n",
    "sns.lineplot(clusters,score)\n",
    "plt.xlabel(\"k\")\n",
    "plt.ylabel(\"score\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "limited-aluminum",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.264083</td>\n",
       "      <td>0.479221</td>\n",
       "      <td>0.661907</td>\n",
       "      <td>0.134295</td>\n",
       "      <td>0.251450</td>\n",
       "      <td>0.105409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.869573</td>\n",
       "      <td>0.174954</td>\n",
       "      <td>0.226061</td>\n",
       "      <td>0.224904</td>\n",
       "      <td>0.050075</td>\n",
       "      <td>0.074002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicassen\n",
       "0  0.264083  0.479221  0.661907  0.134295          0.251450    0.105409\n",
       "1  0.869573  0.174954  0.226061  0.224904          0.050075    0.074002"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_=modle[0]\n",
    "center=pd.DataFrame(kmeans_.cluster_centers_,columns=[\"Fresh\",\"Milk\",\"Grocery\",\"Frozen\",\"Detergents_Paper\",\"Delicassen\"])\n",
    "center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "integral-divorce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26d2e5a11c8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "km_labels=pd.Series(kmeans_.labels_)\n",
    "sizes=km_labels.value_counts(1).values\n",
    "labels=km_labels.value_counts(1).index\n",
    "plt.pie(sizes,autopct=\"%.f%%\")\n",
    "plt.legend(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "activated-province",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), [Text(0, 0, '0'), Text(1, 0, '1')])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "center.plot(kind=\"bar\")\n",
    "plt.xticks(rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "lasting-april",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "round-vietnam",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dried-adolescent",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
